There exist systems for accomplishing automatic authentication or identification of a person using his/her fingerprint. A fingerprint of a person comprises a distinctive and unique ridge pattern structure. For authentication or identification purposes, this ridge pattern structure can be characterized by endings and bifurcations of the individual ridges. These features are popularly known as minutiae.
An example fingerprint is shown in FIG. 1A. The minutiae for the fingerprint shown in FIG. 1A are shown in FIG. 1B as being enclosed by "boxes." For example, box 101B shows a bifurcation minutiae of a bifurcated ridge 101A and box 103B shows a ridge ending minutiae of ridge 103A. The ridge ending and ridge bifurcation minutiae are also illustrated as 1101B and 1103B in the schematic of a fingerprint shown in FIG. 11. Note that minutiae on the ridges in fingerprints have directions (also called orientations) 105 associated with them. Ridge orientation of ridge ending minutiae is also illustrated as 1105 in FIG. 11. The direction of a minutiae at a ridge end 103B is the direction in which the end of the ridge points. The direction of a bifurcation minutiae 101B is the direction in which the bifurcated ridge points. Minutiae also have locations which are the positions, with respect to some coordinate system, of the minutiae on the fingerprint.
One of the prevalent methods of fingerprint authentication and identification methods is based on minutiae features. These systems need to process the fingerprint images to obtain accurate and reliable minutiae features to effectively determine the identity of a person.
FIG. 2 is a flow chart showing the steps generally performed by a typical prior art system 200.
In step 210, the image is acquired. This acquisition of the image could either be through a CCD camera and framegrabber interface or through a document scanner communicating with the primary computing equipment.
Once the image is acquired into the computer memory or disk, relevant minutiae features are extracted (220). Not all of the features thus extracted are reliable; some of the unreliable features are optionally edited or pruned (step 230), e.g., through manual intervention. The resultant reliable features are used for matching the fingerprint images (step 240).
In semi-automatic systems, the unreliable features could be manually pruned by a human expert through visual inspection before the minutiae are used for matching (step 240). The following reference mentions such manual pruning system incorporated into an automatic fingerprint identification system:
Advances in Fingerprint Technology, PA1 Edited by Henry C. Lee, R. E. Gaensslen, PA1 Published by CRC press, Ann Arbor, PA1 Chapter on Automated Fingerprint Identification Systems, PA1 I. North American Morpho Systems, PA1 Section on Fingerprint Processing Functions. PA1 Nalini K. Ratha and Shaoyun Chen and Anil K. Jain, PA1 Adaptive flow orientation based texture extraction in fingerprint images PA1 Pattern Recognition, PA1 vol. 28, no. 11, pp. 1657-1672, November, 1995.
This reference is herein incorporated by reference in its entirety.
The fingerprint feature extraction 220, pruning 230, and matching system 240 constitute the primary backbone 250 of a typical minutiae-based automatic fingerprint identification systems (AFIS). The matching results are typically verified by a human expert (step 260). The verification may also be performed automatically. The following reference describes examples of the state of the prior art:
This reference is herein incorporated by reference in its entirety.
FIG. 3 is a flow chart showing the prior art steps performed by a feature extraction process 220 that are similar to some of the feature extraction methods proposed by Ratha, Jain, and Chen in the article incorporated above.
It is often not desirable to directly use the input fingerprint image for feature extraction. The fingerprint image might need an enhancement or preprocessing before one could further extract minutiae. Typically, a smoothing process is employed to reduce the pixel-wise noise (step 305).
After the preprocessing stages, prior art systems find the directions of the ridge flow (step 310). The next important step in the processing is finding the exact location of the finger in the image. To accomplish this process referred to as the foreground/background segmentation (step 315) separates the finger part of the image from the background part of the image. Once the finger part is localized, i.e., segmented to define its location, the next step is to extract the ridges from the fingerprint image (step 320). The ridges thus extracted are thick and might contain some noisy artifacts which do not correspond to any meaningful structures on the finger. These small structures, i.e., the noisy artifacts, can be safely removed and the longer structures are smoothed (step 325). The longer structures are thinned to one-pixel width and then processed to remove any other artifacts using morphological operators (step 330). The locations and orientations of ridge endings and bifurcations are then extracted from the thinned structures (step 335) to obtain the minutiae. In some systems, a "cleanup" or post processing 340 is performed. Here undesirable minutiae are removed based on some criteria.